改进的采样方法让贝叶斯优化更快更准
Fast, Precise Thompson Sampling for Bayesian Optimization
- 用改进的随机采样策略更精准定位最优解
- 在多个测试函数上比传统方法更快收敛
- 适合追求高效精准优化的科研与工程场景
Thompson采样（TS）在多臂赌博机问题中具有最优遗憾率和优异的实验表现，但在贝叶斯优化中表现逊于常见采集函数（如EI、UCB）。TS根据各选项为最优的概率进行采样。近期算法P-Star Sampler（PSS）通过击中-运行（Hit-and-Run）实现该采样。本文提出改进版本Stagger Thompson Sampler（STS），在计算时间更少的前提下，比TS更精确地定位最大值。在多种测试函数、广泛维度范围的数值实验中，STS优于TS、PSS及其他采集方法。此外，由于PSS最初并非作为独立采集函数，而是作为批处理算法最小终方差（MTV）的输入，我们还证明：当作为MTV输入时，STS性能可媲美PSS。
原文摘要 · Abstract (English)
Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms according to the probability that they are optimal. A recent algorithm, P-Star Sampler (PSS), performs such a sampling via Hit-and-Run. We present an improved version, Stagger Thompson Sampler (STS). STS more precisely locates the maximizer than does TS using less computation time. We demonstrate that STS outperforms TS, PSS, and other acquisition methods in numerical experiments of optimizations of several test functions across a broad range of dimension. Additionally, since PSS was originally presented not as a standalone acquisition method but as an input to a batching algorithm called Minimal Terminal Variance (MTV), we also demon-strate that STS matches PSS performance when used as the input to MTV.
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